'data.frame': 58654 obs. of 28 variables:
$ hid : int 1 1 2 2 3 4 4 4 5 6 ...
$ region : Ord.factor w/ 9 levels "Burgenland"<"Lower Austria"<..: 6 6 5 5 5 6 6 6 3 2 ...
$ hsize : Factor w/ 9 levels "1","2","3","4",..: 2 2 2 2 1 3 3 3 1 5 ...
$ eqsize : num 1.5 1.5 1.5 1.5 1 1.8 1.8 1.8 1 2.6 ...
$ eqIncome : num [1:58654(1d)] 11128 11128 19695 19695 5066 ...
..- attr(*, "dimnames")=List of 1
.. ..$ : chr [1:58654] "2592313" "2592313" "2045000" "2045000" ...
$ pid : int 1 2 1 2 1 1 2 3 1 1 ...
$ id : chr "0000101" "0000102" "0000201" "0000202" ...
$ age : num 25 24 57 53 30 32 33 8 77 34 ...
$ gender : Factor w/ 2 levels "male","female": 1 2 2 1 2 1 2 1 2 2 ...
$ ecoStat : Factor w/ 7 levels "1","2","3","4",..: 1 4 1 1 6 1 1 NA 5 2 ...
$ citizenship: Factor w/ 3 levels "AT","EU","Other": 3 1 1 1 1 1 1 NA 1 1 ...
$ py010n : num 16693 0 0 16884 0 ...
$ py050n : num 0 0 12565 0 0 ...
$ py090n : num 0 0 0 0 5066 ...
$ py100n : num 0 0 0 0 0 ...
$ py110n : num 0 0 0 0 0 0 0 NA 0 0 ...
$ py120n : num 0 0 0 0 0 0 0 NA 0 0 ...
$ py130n : num 0 0 0 0 0 0 0 NA 0 0 ...
$ py140n : num 0 0 0 0 0 0 0 NA 0 0 ...
$ hy040n : num 0 0 0 0 0 0 0 0 0 0 ...
$ hy050n : num 0 0 0 0 0 ...
$ hy070n : num 0 0 0 0 0 0 0 0 0 0 ...
$ hy080n : num 0 0 0 0 0 0 0 0 0 0 ...
$ hy090n : num 0 0 0 0 0 ...
$ hy110n : num 0 0 0 0 0 ...
$ hy130n : num 0 0 93.6 93.6 0 ...
$ hy145n : num 0 0 -187 -187 0 ...
$ main : logi TRUE FALSE FALSE TRUE TRUE TRUE ...
hid region hsize eqsize
Min. : 1 Vienna :11657 2 :14128 Min. :1.000
1st Qu.: 6262 Lower Austria:11127 4 :13180 1st Qu.:1.500
Median :12465 Upper Austria:10310 3 :12429 Median :2.000
Mean :12488 Styria : 8142 1 : 8602 Mean :1.943
3rd Qu.:18719 Tyrol : 4796 5 : 6745 3rd Qu.:2.400
Max. :25000 Carinthia : 4111 6 : 2094 Max. :4.500
(Other) : 8511 (Other): 1476
eqIncome pid id age
Min. : 0 Min. :1.00 Length:58654 Min. :-1.00
1st Qu.: 13539 1st Qu.:1.00 Class :character 1st Qu.:22.00
Median : 18322 Median :2.00 Mode :character Median :40.00
Mean : 20163 Mean :2.07 Mean :39.75
3rd Qu.: 24277 3rd Qu.:3.00 3rd Qu.:57.00
Max. :179946 Max. :9.00 Max. :97.00
gender ecoStat citizenship py010n py050n
male :28539 1 :20900 AT :44066 Min. : 0 Min. : -6895
female:30115 5 :12836 EU : 1257 1st Qu.: 0 1st Qu.: 0
7 : 4607 Other: 3162 Median : 2382 Median : 0
2 : 4362 NA's :10169 Mean : 9062 Mean : 1288
4 : 2921 3rd Qu.: 16820 3rd Qu.: 0
(Other): 2859 Max. :199075 Max. :129874
NA's :10169 NA's :10169 NA's :10169
py090n py100n py110n py120n
Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.00
1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.00
Median : 0.0 Median : 0 Median : 0.0 Median : 0.00
Mean : 444.6 Mean : 3713 Mean : 72.9 Mean : 51.22
3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0.00
Max. :29887.1 Max. :101777 Max. :22546.8 Max. :46398.44
NA's :10169 NA's :10169 NA's :10169 NA's :10169
py130n py140n hy040n hy050n
Min. : 0.0 Min. : 0.00 Min. : -2962.5 Min. :-11857
1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0
Median : 0.0 Median : 0.00 Median : 0.0 Median : 0
Mean : 393.7 Mean : 41.73 Mean : 879.9 Mean : 2826
3rd Qu.: 0.0 3rd Qu.: 0.00 3rd Qu.: 0.0 3rd Qu.: 4558
Max. :53183.6 Max. :18643.46 Max. :129586.6 Max. :118309
NA's :10169 NA's :10169
hy070n hy080n hy090n hy110n
Min. : 0.00 Min. : 0.0 Min. : -457.46 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.75 1st Qu.: 0.00
Median : 0.00 Median : 0.0 Median : 58.45 Median : 0.00
Mean : 93.12 Mean : 744.6 Mean : 462.45 Mean : 32.97
3rd Qu.: 0.00 3rd Qu.: 0.0 3rd Qu.: 234.78 3rd Qu.: 0.00
Max. :17954.97 Max. :124206.2 Max. :112011.03 Max. :14506.49
hy130n hy145n main
Min. :-5490 Min. :-29519.3 Mode :logical
1st Qu.: 0 1st Qu.: -256.8 FALSE:33654
Median : 0 Median : 0.0 TRUE :25000
Mean : 339 Mean : -108.8
3rd Qu.: 0 3rd Qu.: 0.0
Max. :40763 Max. : 49768.0